In this paper, phone-level mixup is proposed to make full use of smaller unit (e.g., phone) to creates arbitrary bigger unit (e.g., word) with various pronunciation scores. Experiments conducted on Speechocean762 show that proposed augmented data is complementary to the real non-native data for training word-level pronunciation scorer. Moreover, feature combination under the proposed training framework brings extra improvement. In the future, we will extend current phone-level mixup to word-level mixup for improving sentence-level scoring. Meanwhile, we will also investigate whether similar improvement could be achieved, when the proposed data augmentation method is applied to attention-based pronunciation scorers [1]}, [2]}.
